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import os |
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import random |
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import numpy as np |
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import torch |
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import torch.utils.data |
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from tqdm import tqdm |
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from uvr5_pack.lib_v5 import spec_utils |
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class VocalRemoverValidationSet(torch.utils.data.Dataset): |
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def __init__(self, patch_list): |
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self.patch_list = patch_list |
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def __len__(self): |
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return len(self.patch_list) |
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def __getitem__(self, idx): |
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path = self.patch_list[idx] |
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data = np.load(path) |
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X, y = data['X'], data['y'] |
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X_mag = np.abs(X) |
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y_mag = np.abs(y) |
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return X_mag, y_mag |
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def make_pair(mix_dir, inst_dir): |
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input_exts = ['.wav', '.m4a', '.mp3', '.mp4', '.flac'] |
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X_list = sorted([ |
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os.path.join(mix_dir, fname) |
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for fname in os.listdir(mix_dir) |
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if os.path.splitext(fname)[1] in input_exts]) |
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y_list = sorted([ |
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os.path.join(inst_dir, fname) |
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for fname in os.listdir(inst_dir) |
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if os.path.splitext(fname)[1] in input_exts]) |
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filelist = list(zip(X_list, y_list)) |
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return filelist |
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def train_val_split(dataset_dir, split_mode, val_rate, val_filelist): |
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if split_mode == 'random': |
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filelist = make_pair( |
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os.path.join(dataset_dir, 'mixtures'), |
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os.path.join(dataset_dir, 'instruments')) |
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random.shuffle(filelist) |
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if len(val_filelist) == 0: |
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val_size = int(len(filelist) * val_rate) |
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train_filelist = filelist[:-val_size] |
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val_filelist = filelist[-val_size:] |
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else: |
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train_filelist = [ |
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pair for pair in filelist |
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if list(pair) not in val_filelist] |
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elif split_mode == 'subdirs': |
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if len(val_filelist) != 0: |
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raise ValueError('The `val_filelist` option is not available in `subdirs` mode') |
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train_filelist = make_pair( |
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os.path.join(dataset_dir, 'training/mixtures'), |
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os.path.join(dataset_dir, 'training/instruments')) |
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val_filelist = make_pair( |
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os.path.join(dataset_dir, 'validation/mixtures'), |
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os.path.join(dataset_dir, 'validation/instruments')) |
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return train_filelist, val_filelist |
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def augment(X, y, reduction_rate, reduction_mask, mixup_rate, mixup_alpha): |
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perm = np.random.permutation(len(X)) |
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for i, idx in enumerate(tqdm(perm)): |
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if np.random.uniform() < reduction_rate: |
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y[idx] = spec_utils.reduce_vocal_aggressively(X[idx], y[idx], reduction_mask) |
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if np.random.uniform() < 0.5: |
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X[idx] = X[idx, ::-1] |
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y[idx] = y[idx, ::-1] |
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if np.random.uniform() < 0.02: |
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X[idx] = X[idx].mean(axis=0, keepdims=True) |
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y[idx] = y[idx].mean(axis=0, keepdims=True) |
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if np.random.uniform() < 0.02: |
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X[idx] = y[idx] |
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if np.random.uniform() < mixup_rate and i < len(perm) - 1: |
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lam = np.random.beta(mixup_alpha, mixup_alpha) |
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X[idx] = lam * X[idx] + (1 - lam) * X[perm[i + 1]] |
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y[idx] = lam * y[idx] + (1 - lam) * y[perm[i + 1]] |
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return X, y |
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def make_padding(width, cropsize, offset): |
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left = offset |
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roi_size = cropsize - left * 2 |
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if roi_size == 0: |
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roi_size = cropsize |
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right = roi_size - (width % roi_size) + left |
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return left, right, roi_size |
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def make_training_set(filelist, cropsize, patches, sr, hop_length, n_fft, offset): |
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len_dataset = patches * len(filelist) |
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X_dataset = np.zeros( |
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(len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64) |
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y_dataset = np.zeros( |
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(len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64) |
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for i, (X_path, y_path) in enumerate(tqdm(filelist)): |
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X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft) |
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coef = np.max([np.abs(X).max(), np.abs(y).max()]) |
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X, y = X / coef, y / coef |
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l, r, roi_size = make_padding(X.shape[2], cropsize, offset) |
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X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode='constant') |
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y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode='constant') |
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starts = np.random.randint(0, X_pad.shape[2] - cropsize, patches) |
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ends = starts + cropsize |
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for j in range(patches): |
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idx = i * patches + j |
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X_dataset[idx] = X_pad[:, :, starts[j]:ends[j]] |
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y_dataset[idx] = y_pad[:, :, starts[j]:ends[j]] |
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return X_dataset, y_dataset |
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def make_validation_set(filelist, cropsize, sr, hop_length, n_fft, offset): |
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patch_list = [] |
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patch_dir = 'cs{}_sr{}_hl{}_nf{}_of{}'.format(cropsize, sr, hop_length, n_fft, offset) |
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os.makedirs(patch_dir, exist_ok=True) |
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for i, (X_path, y_path) in enumerate(tqdm(filelist)): |
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basename = os.path.splitext(os.path.basename(X_path))[0] |
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X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft) |
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coef = np.max([np.abs(X).max(), np.abs(y).max()]) |
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X, y = X / coef, y / coef |
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l, r, roi_size = make_padding(X.shape[2], cropsize, offset) |
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X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode='constant') |
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y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode='constant') |
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len_dataset = int(np.ceil(X.shape[2] / roi_size)) |
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for j in range(len_dataset): |
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outpath = os.path.join(patch_dir, '{}_p{}.npz'.format(basename, j)) |
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start = j * roi_size |
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if not os.path.exists(outpath): |
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np.savez( |
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outpath, |
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X=X_pad[:, :, start:start + cropsize], |
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y=y_pad[:, :, start:start + cropsize]) |
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patch_list.append(outpath) |
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return VocalRemoverValidationSet(patch_list) |
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